Nearly half of companies now report measurable value from artificial intelligence, according to the Boston Consulting Group’s Applied AI Index 2026, a survey of 1,330 executives across more than twenty sectors. Only 7.5% of the companies surveyed, however, belong to the category of the most advanced organizations, which BCG calls future-built, while 41% are scaling their AI applications, with value potential still untapped.
Companies that are strong on both strategic clarity and the application of AI within their processes report, on average, five times more value from AI.
Admittedly, this finding does not demonstrate causation; it does indicate, however, that
technological capabilities do not convert automatically into performance.
For an executive committee, the useful question concerns the path that leads from a technical capability to a financial result. Demand forecasting offers a way to walk through that path step by step.
A Better Forecast Does Not, on Its Own, Reduce Working Capital
Demand forecasting is among the most frequently cited applications of AI in industry and distribution. Anticipating future volumes more precisely can improve product availability, reduce excess inventory and limit the costs of stockouts and urgent replenishment. Current predictive methods make this prospect credible in many environments, provided that suitable data is available.
A more accurate forecast is, first of all, a piece of information, and it changes working capital only if it leads to different decisions on procurement, production or inventory management. The company must be able to adjust its order quantities, replenishment frequencies or safety stocks, while respecting its capacity constraints, supplier lead times and service commitments.
Several obstacles can stand in the way: procurement parameters that remain unchanged, high contractual minimum quantities, long supplier lead times, or purchasing and planning teams that have neither the responsibility nor the means to change those parameters. In each of these cases, the forecast becomes more accurate while inventory does not decrease, and the model, however statistically performant, produces no financial effect.
From Inventory Reduction to the Income Statement
Suppose the improved forecast does translate into different decisions. The financial effect still needs to be qualified, because an inventory reduction acts first on the balance sheet.
Take, by way of illustration, a company with inventory of €20 million. The improved forecast allows it to reduce inventory durably by 10%, which releases €2 million of cash. This effect is real, but it is one-off: it occurs once, when inventory falls, and it improves working capital without directly changing earnings.
The income statement benefits only from the holding costs avoided on those €2 million: financing cost, storage, handling, insurance and obsolescence. With an annual holding cost rate of 15%, an assumption made for this example, the recurring saving amounts to €0.3 million per year. If the solution costs €0.25 million per year in integration, operation, supervision and maintenance, the net effect on earnings is limited to €0.05 million.
These orders of magnitude change the nature of the discussion. The €2 million of cash may justify the investment, particularly for a company facing a liquidity constraint or bank covenants, but the project then does not represent a margin gain and should not appear as one in a business plan. A project presented as generating €2 million of savings, by contrast, risks disappointment at the first performance review.
Two further effects still need to be included: lost sales avoided thanks to better availability, which work in the project’s favor, and the risk of a lower service level if inventory is reduced too aggressively, which works against it.
What This Example Reveals About Other AI Projects
The same reasoning applies to many initiatives. A customer risk model creates value only through the prevention or collection actions it triggers, through their effectiveness and through their cost. Likewise, a pricing support tool generates margin if customers accept the recommended prices and if the volumes retained offset the price effect. Where an automation cuts by 40% the time of an operation that represents 20% of a process’s total time, the overall process gains 8%, and that gain becomes a saving only if teams can redeploy or remove the capacity released.
In each case, the evaluation links four elements: the starting situation, the improved decision, the action taken and its economic effect, net of costs and risks. It requires establishing a baseline before the intervention, measuring the effects obtained, using full costs (technology, data, integration, process transformation, training, supervision and maintenance) and comparing AI with alternative solutions, which sometimes deliver a comparable improvement at lower cost.
The Conditions for Conversion
Choosing Where to Intervene
A company’s financial, technological and managerial resources are limited, and every AI initiative places them in competition with other investment projects and with the execution of existing activities. An accumulation of pilots becomes counterproductive when it fragments teams, multiplies integration costs and delays the transformation of the processes that matter most.
BCG’s observations point in the same direction. Future-built companies concentrate their effort in a coherent program aimed at their main priorities, and 61% of them have moved from isolated pilots to a multi-year, enterprise-wide program. BCG also recommends devoting about 70% of the transformation effort to people, processes and organization, against 20% for technology and data and 10% for algorithms. The teachings of MIT’s AI Strategy and Leadership program are consistent with this finding, in emphasizing the interdependence of strategy, data, technology, organization and leadership.
Selection requires distinguishing the problems that justify AI from those that better data quality, more reliable reporting, process simplification or rule-based automation would solve at lower cost. Each opportunity is assessed on its potential economic contribution, data availability, operational feasibility, execution costs and associated risks. A promising initiative may be deferred when the conditions for deployment are not in place, while a more modest project deserves to be launched quickly when its value is identifiable, its data available and its integration under control.
Experimentation remains useful for understanding the possibilities and limits of the technologies, provided it is given a precise function: testing hypotheses, reducing uncertainty and preparing investment decisions. An AI strategy is thus distinguished from a portfolio of experiments by explicit priorities and by management’s ability to give up certain initiatives.
A Leadership Responsibility
Steering AI investments falls within strategy, capital allocation, work organization and risk management, beyond the technology functions alone. Business teams identify the decisions to improve and take ownership of operational results; technology teams ensure reliability and integration; the finance function establishes the economic assumptions, consolidates costs and distinguishes potential benefits from results actually captured, as the inventory example illustrates; and general management ensures consistency of priorities and settles trade-offs between functions.
This allocation of roles is not enough if initiatives are assessed only at launch. A transformation rarely creates all its value at deployment, because the effects depend on adoption, on how processes evolve and on adjustments made on the basis of observed results. An initiative whose benefits fail to materialize must be open to correction, refocusing or termination, while a solution whose contribution is demonstrated may justify further investment.
Agentic AI reinforces this requirement. According to BCG, 42% of companies plan to give agents genuine decision-making authority by 2030, while governance is progressing more slowly than deployment. When systems intervene directly in processes, the limits of delegation and the control mechanisms determine the solution’s economic potential as much as its technical performance, and governance then helps preserve the value created.
As access to tools becomes widespread, performance gaps between companies may come to rest on the quality of investment decisions: choosing the right use cases, adapting processes and measuring results against a baseline. A company may have an ambitious AI strategy without yet having a credible strategy for capturing value, and the difference deserves the explicit attention of general management, finance leadership and boards of directors, since the success of an AI transformation will be judged by its verifiable contribution to cash, margin and growth, net of the costs and risks incurred.
